Spatiotemporal Signal

Spatiotemporal signal processing focuses on analyzing data exhibiting variations across both space and time, aiming to extract meaningful patterns and insights from complex datasets. Current research emphasizes developing advanced models, such as graph convolutional networks, transformers, and neural fields, to effectively capture intricate spatiotemporal dependencies, often incorporating techniques like dynamic mode decomposition and optimal transport for improved accuracy and interpretability. These advancements are crucial for diverse applications, including traffic forecasting, signal recovery from incomplete sensor data, and neural decoding from brain activity, ultimately improving efficiency and accuracy in various scientific and engineering domains.